restrictedItems/predict.py

from transformers import BertTokenizer, BertForSequenceClassification
import torch

# Load the trained model and tokenizer
# model = BertForSequenceClassification.from_pretrained("/Users/slei/hackweek2024-sup-genai-tools/spaces/restricted_item_detector/trained_model")
model = BertForSequenceClassification.from_pretrained("sleiyer/restricted_item_detector")
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Function to predict the class of a single input text
def predict(text):
  # Preprocess the input text
  inputs = tokenizer(text, return_tensors='pt', truncation=True, padding=True)

  # Make predictions
  with torch.no_grad():
    outputs = model(**inputs)

  # Get the predicted class
  logits = outputs.logits
  predicted_class = torch.argmax(logits, dim=1).item()

  return predicted_class

label_map = {0: 'Allowed Item', 1: 'Restricted Item'}

def main():
  while True:
    # Prompting the user for input
    user_input = input("Enter something: ")

    predicted_class = predict(user_input)

    # Map the predicted class to a human-readable label
    predicted_label = label_map[predicted_class]

    # Displaying the user input
    print(f'The item "{user_input}" is classified as: "{predicted_label}"')

if __name__ == "__main__":
  main()